# Key Features:

* **Modern DiT Architecture** with robust multi-encoder text conditioning
* **Triple Text Encoder Support**: T5-XXL + CLIP ViT-L + ViT-bigG for richer prompt understanding
* **Advanced Training Objectives**: Flow Matching, VP Diffusion, Optimal Transport (OT), REPA, and more
* **Holy Grail Adaptive Sampler**: A highly sophisticated sampling system featuring per-step CFG scheduling, dynamic adapter control, CADS annealing, and intelligent noise handling for superior generation quality and consistency
* **Advanced Adapter System**: Deep multi-LoRA / DoRA / LyCORIS stacking with depth-aware routing for flexible and powerful model customization
* **Rich Inference Capabilities**: img2img, reference token injection, speculative CFG, Self-Attention Guidance (SAG), and high-quality generation tools built-in
* **Clean & Reproducible Setup**: Well-structured codebase, configuration snapshots, run manifests, bf16 + torch.compile support, and DDP training

The framework is extensively documented, including detailed explanations of the generation pipeline and the Holy Grail sampler.

**GitHub:** [https://github.com/Llunarstack/sdx](https://github.com/Llunarstack/sdx)

If you're working with Diffusion Transformers or exploring Flow Matching and advanced sampling techniques, SDX provides a solid, forward-looking foundation.

Note: The model hasn't been trained; therefore, there are no results to verify its superiority over current models.

SDX (Stable Diffusion Extreme) – A Clean, Modern Diffusion Transformer Framework Built with the Latest Techniques and Targeted Fixes